Abstract: This talk will address how advances in Machine Learning, especially used within a “Big Data” paradigm and exploiting new data generation and capture capabilities, are positioned to catalyze radical advances of Medical Science and the transformation of Healthcare.

In the first part of the talk Dr. Aliferis will present several prototypical examples from the broad areas of Precision Medicine and Healthcare optimization and re-engineering. As with every paradigm shift, traditional cultural gaps and incentive differences need be addressed for the technological potential to be fully realized.

In the second part of the talk Dr. Aliferis will address effective strategic and tactical approaches for bridging the gap between academic work in Machine Learning and affecting patients. On the strategy side he will make a case for careful assessment and prioritization on the basis of quality-of-care and economic impact of machine learning modalities; he will also emphasize thematic coherency, urgency, and intensity. From a tactical perspective Dr. Aliferis will discuss fundamental differences between purely scientific/exploratory and applied/“clinical grade” work and then he will briefly address: quality control for new method development that “works in real life”; benchmarking for evidence-based machine learning; standardization, best practices and regulation of ML technologies in healthcare; bottom-up and top-down ideation; R&D acceleration, IP protection, and enhancing traditional academic incentives.